'''simple docstring'''





import warnings
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE_:	Dict    =logging.get_logger(__name__)




class 			__A (					UpperCamelCase__      ):
    a__			:		List[Any]			  =    ["""input_ids""", """attention_mask"""]



    def __init__(self       :       Optional[Any]  ,     __a       :       int="</s>"  ,     __a       :       Optional[Any]="<unk>"  ,     __a       :       str="<pad>"  ,     __a       :       Dict=125  ,     __a       :       Optional[Any]=None  ,     **__a       :       List[Any]  ,     ):
         # Add extra_ids to the special token list
         if extra_ids > 0 and additional_special_tokens is None:
              UpperCAmelCase_			       =  [f"""<extra_id_{i}>""" for i in range(__a					)]
         elif extra_ids > 0 and additional_special_tokens is not None:
              # Check that we have the right number of extra_id special tokens
              UpperCAmelCase_			       =  len(set(filter(lambda __a					: bool("extra_id" in str(__a					)					)  ,     __a					)					)					)
              if extra_tokens != extra_ids:
                   raise ValueError(
                       f"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
                       " provided to ByT5Tokenizer. In this case the additional_special_tokens must include the"
                       " extra_ids tokens"					)

         UpperCAmelCase_			       =  AddedToken(__a  ,     lstrip=__a  ,     rstrip=__a					) if isinstance(__a  ,     __a					) else pad_token
         UpperCAmelCase_			       =  AddedToken(__a  ,     lstrip=__a  ,     rstrip=__a					) if isinstance(__a  ,     __a					) else eos_token
         UpperCAmelCase_			       =  AddedToken(__a  ,     lstrip=__a  ,     rstrip=__a					) if isinstance(__a  ,     __a					) else unk_token

         super().__init__(
             eos_token=__a  ,     unk_token=__a  ,     pad_token=__a  ,     extra_ids=__a  ,     additional_special_tokens=__a  ,     **__a  ,     )

         UpperCAmelCase_			       =  extra_ids

         UpperCAmelCase_			       =  2**8  # utf is 8 bits

         # define special tokens dict
         UpperCAmelCase_			       =  {
             self.pad_token: 0,
             self.eos_token: 1,
             self.unk_token: 2,
         }
         UpperCAmelCase_			       =  len(self.special_tokens_encoder					)
         UpperCAmelCase_			       =  len(__a					)
         for i, token in enumerate(__a					):
              UpperCAmelCase_			       =  self.vocab_size + i - n
         UpperCAmelCase_			       =  {v: k for k, v in self.special_tokens_encoder.items()}



    @property
    def    _lowercase		(self       :       int					):
         return self._utf_vocab_size + self._num_special_tokens + self._extra_ids



    def    _lowercase		(self       :       Union[str, Any]  ,     __a       :       List[int]  ,     __a       :       Optional[List[int]] = None  ,     __a       :       bool = False					):
         if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=__a  ,     token_ids_a=__a  ,     already_has_special_tokens=__a					)

         # normal case: some special tokens
         if token_ids_a is None:
              return ([0] * len(__a					)) + [1]
         return ([0] * len(__a					)) + [1] + ([0] * len(__a					)) + [1]



    def    _lowercase		(self       :       int  ,     __a       :       List[int]					):
         if len(__a					) > 0 and token_ids[-1] == self.eos_token_id:
              warnings.warn(
                  f"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
                  " eos tokens being added."					)
              return token_ids
         else:
              return token_ids + [self.eos_token_id]



    def    _lowercase		(self       :       Tuple  ,     __a       :       List[int]  ,     __a       :       Optional[List[int]] = None					):
         UpperCAmelCase_			       =  [self.eos_token_id]

         if token_ids_a is None:
              return len(token_ids_a + eos					) * [0]
         return len(token_ids_a + eos + token_ids_a + eos					) * [0]



    def    _lowercase		(self       :       Union[str, Any]  ,     __a       :       List[int]  ,     __a       :       Optional[List[int]] = None					):
         UpperCAmelCase_			       =  self._add_eos_if_not_present(__a					)
         if token_ids_a is None:
              return token_ids_a
         else:
              UpperCAmelCase_			       =  self._add_eos_if_not_present(__a					)
              return token_ids_a + token_ids_a



    def    _lowercase		(self       :       List[Any]  ,     __a       :       str					):
         UpperCAmelCase_			       =  [chr(__a					) for i in text.encode("utf-8"					)]
         return tokens



    def    _lowercase		(self       :       Union[str, Any]  ,     __a       :       Union[str, Any]					):
         if token in self.special_tokens_encoder:
              UpperCAmelCase_			       =  self.special_tokens_encoder[token]
         elif token in self.added_tokens_encoder:
              UpperCAmelCase_			       =  self.added_tokens_encoder[token]
         elif len(__a					) != 1:
              UpperCAmelCase_			       =  self.unk_token_id
         else:
              UpperCAmelCase_			       =  ord(__a					) + self._num_special_tokens
         return token_id



    def    _lowercase		(self       :       Optional[int]  ,     __a       :       int					):
         if index in self.special_tokens_decoder:
              UpperCAmelCase_			       =  self.special_tokens_decoder[index]
         else:
              UpperCAmelCase_			       =  chr(index - self._num_special_tokens					)
         return token



    def    _lowercase		(self       :       Dict  ,     __a       :       Optional[int]					):
         UpperCAmelCase_			       =  B""
         for token in tokens:
              if token in self.special_tokens_decoder:
                   UpperCAmelCase_			       =  self.special_tokens_decoder[token].encode("utf-8"					)
              elif token in self.added_tokens_decoder:
                   UpperCAmelCase_			       =  self.special_tokens_decoder[token].encode("utf-8"					)
              elif token in self.special_tokens_encoder:
                   UpperCAmelCase_			       =  token.encode("utf-8"					)
              elif token in self.added_tokens_encoder:
                   UpperCAmelCase_			       =  token.encode("utf-8"					)
              else:
                   UpperCAmelCase_			       =  bytes([ord(__a					)]					)
              bstring += tok_string
         UpperCAmelCase_			       =  bstring.decode("utf-8"  ,     errors="ignore"					)
         return string






    def    _lowercase		(self       :       Dict  ,     __a       :       str  ,     __a       :       Optional[str] = None					):
         return ()











"""simple docstring"""





import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE__   =  logging.getLogger(__name__)

@dataclass
class 						lowercase   :
      _SCREAMING_SNAKE_CASE							      = 42
      _SCREAMING_SNAKE_CASE							      = 42
      _SCREAMING_SNAKE_CASE							      = 42







@dataclass
class 						lowercase   :
      _SCREAMING_SNAKE_CASE							      = 42
      _SCREAMING_SNAKE_CASE							      = 42
      _SCREAMING_SNAKE_CASE							      = None
      _SCREAMING_SNAKE_CASE							      = None







class 						lowercase   (   _UpperCAmelCase   ):
      _SCREAMING_SNAKE_CASE							      = 'train'
      _SCREAMING_SNAKE_CASE							      = 'dev'
      _SCREAMING_SNAKE_CASE							      = 'test'







class 						lowercase   :





      @staticmethod
      def        _snake_case     (			lowercase ,		lowercase					)						->	List[InputExample]:
            raise NotImplementedError





      @staticmethod
      def        _snake_case     (			lowercase					)						->	List[str]:
            raise NotImplementedError





      @staticmethod
      def        _snake_case     (			lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase=False ,		lowercase="[CLS]" ,		lowercase=1 ,		lowercase="[SEP]" ,		lowercase=False ,		lowercase=False ,		lowercase=0 ,		lowercase=0 ,		lowercase=-100 ,		lowercase=0 ,		lowercase=True ,		)						->	List[InputFeatures]:
            lowerCAmelCase    		=			{label: i for i, label in enumerate(lowercase					)}

            lowerCAmelCase    		=			[]
            for ex_index, example in enumerate(lowercase					):
                  if ex_index % 10_000 == 0:
                        logger.info("""Writing example %d of %d""" ,		lowercase ,		len(lowercase					)					)

                  lowerCAmelCase    		=			[]
                  lowerCAmelCase    		=			[]
                  for word, label in zip(example.words ,		example.labels					):
                        lowerCAmelCase    		=			tokenizer.tokenize(lowercase					)

                        # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
                        if len(lowercase					) > 0:
                              tokens.extend(lowercase					)
                              # Use the real label id for the first token of the word, and padding ids for the remaining tokens
                              label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(lowercase					) - 1)					)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
                  lowerCAmelCase    		=			tokenizer.num_special_tokens_to_add()
                  if len(lowercase					) > max_seq_length - special_tokens_count:
                        lowerCAmelCase    		=			tokens[: (max_seq_length - special_tokens_count)]
                        lowerCAmelCase    		=			label_ids[: (max_seq_length - special_tokens_count)]

                  # The convention in BERT is:
                  # (a) For sequence pairs:
                  #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
                  #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
                  # (b) For single sequences:
                  #  tokens:   [CLS] the dog is hairy . [SEP]
                  #  type_ids:   0   0   0   0  0     0   0
                  #
                  # Where "type_ids" are used to indicate whether this is the first
                  # sequence or the second sequence. The embedding vectors for `type=0` and
                  # `type=1` were learned during pre-training and are added to the wordpiece
                  # embedding vector (and position vector). This is not *strictly* necessary
                  # since the [SEP] token unambiguously separates the sequences, but it makes
                  # it easier for the model to learn the concept of sequences.
                  #
                  # For classification tasks, the first vector (corresponding to [CLS]) is
                  # used as the "sentence vector". Note that this only makes sense because
                  # the entire model is fine-tuned.
                  tokens += [sep_token]
                  label_ids += [pad_token_label_id]
                  if sep_token_extra:
                        # roberta uses an extra separator b/w pairs of sentences
                        tokens += [sep_token]
                        label_ids += [pad_token_label_id]
                  lowerCAmelCase    		=			[sequence_a_segment_id] * len(lowercase					)

                  if cls_token_at_end:
                        tokens += [cls_token]
                        label_ids += [pad_token_label_id]
                        segment_ids += [cls_token_segment_id]
                  else:
                        lowerCAmelCase    		=			[cls_token] + tokens
                        lowerCAmelCase    		=			[pad_token_label_id] + label_ids
                        lowerCAmelCase    		=			[cls_token_segment_id] + segment_ids

                  lowerCAmelCase    		=			tokenizer.convert_tokens_to_ids(lowercase					)

                  # The mask has 1 for real tokens and 0 for padding tokens. Only real
                  # tokens are attended to.
                  lowerCAmelCase    		=			[1 if mask_padding_with_zero else 0] * len(lowercase					)

                  # Zero-pad up to the sequence length.
                  lowerCAmelCase    		=			max_seq_length - len(lowercase					)
                  if pad_on_left:
                        lowerCAmelCase    		=			([pad_token] * padding_length) + input_ids
                        lowerCAmelCase    		=			([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                        lowerCAmelCase    		=			([pad_token_segment_id] * padding_length) + segment_ids
                        lowerCAmelCase    		=			([pad_token_label_id] * padding_length) + label_ids
                  else:
                        input_ids += [pad_token] * padding_length
                        input_mask += [0 if mask_padding_with_zero else 1] * padding_length
                        segment_ids += [pad_token_segment_id] * padding_length
                        label_ids += [pad_token_label_id] * padding_length

                  assert len(lowercase					) == max_seq_length
                  assert len(lowercase					) == max_seq_length
                  assert len(lowercase					) == max_seq_length
                  assert len(lowercase					) == max_seq_length

                  if ex_index < 5:
                        logger.info("""*** Example ***"""					)
                        logger.info("""guid: %s""" ,		example.guid					)
                        logger.info("""tokens: %s""" ,		""" """.join([str(lowercase					) for x in tokens]					)					)
                        logger.info("""input_ids: %s""" ,		""" """.join([str(lowercase					) for x in input_ids]					)					)
                        logger.info("""input_mask: %s""" ,		""" """.join([str(lowercase					) for x in input_mask]					)					)
                        logger.info("""segment_ids: %s""" ,		""" """.join([str(lowercase					) for x in segment_ids]					)					)
                        logger.info("""label_ids: %s""" ,		""" """.join([str(lowercase					) for x in label_ids]					)					)

                  if "token_type_ids" not in tokenizer.model_input_names:
                        lowerCAmelCase    		=			None

                  features.append(
                      InputFeatures(
                          input_ids=lowercase ,		attention_mask=lowercase ,		token_type_ids=lowercase ,		label_ids=lowercase					)					)
            return features


if is_torch_available():
       import torch
       from torch import nn
       from torch.utils.data import Dataset

       class 						lowercase   (   _UpperCAmelCase   ):
             _SCREAMING_SNAKE_CASE							      = 42
             _SCREAMING_SNAKE_CASE							      = nn.CrossEntropyLoss().ignore_index





             def __init__(			self ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase = None ,		lowercase=False ,		lowercase = Split.train ,		)						->	List[str]:
                   # Load data features from cache or dataset file
                   lowerCAmelCase    		=			os.path.join(
                       lowercase ,		"""cached_{}_{}_{}""".format(mode.value ,		tokenizer.__class__.__name__ ,		str(lowercase					)					) ,		)

                   # Make sure only the first process in distributed training processes the dataset,
                   # and the others will use the cache.
                   lowerCAmelCase    		=			cached_features_file + """.lock"""
                   with FileLock(lowercase					):
                         if os.path.exists(lowercase					) and not overwrite_cache:
                               logger.info(f'Loading features from cached file {cached_features_file}'					)
                               lowerCAmelCase    		=			torch.load(lowercase					)
                         else:
                               logger.info(f'Creating features from dataset file at {data_dir}'					)
                               lowerCAmelCase    		=			token_classification_task.read_examples_from_file(lowercase ,		lowercase					)
                               # TODO clean up all this to leverage built-in features of tokenizers
                               lowerCAmelCase    		=			token_classification_task.convert_examples_to_features(
                                   lowercase ,		lowercase ,		lowercase ,		lowercase ,		cls_token_at_end=bool(model_type in ["""xlnet"""]					) ,		cls_token=tokenizer.cls_token ,		cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0 ,		sep_token=tokenizer.sep_token ,		sep_token_extra=lowercase ,		pad_on_left=bool(tokenizer.padding_side == """left"""					) ,		pad_token=tokenizer.pad_token_id ,		pad_token_segment_id=tokenizer.pad_token_type_id ,		pad_token_label_id=self.pad_token_label_id ,		)
                               logger.info(f'Saving features into cached file {cached_features_file}'					)
                               torch.save(self.features ,		lowercase					)





             def __len__(			self					)						->	int:
                   return len(self.features					)





             def __getitem__(			self ,		lowercase					)						->	InputFeatures:
                   return self.features[i]


if is_tf_available():
       import tensorflow as tf

       class 						lowercase   :
             _SCREAMING_SNAKE_CASE							      = 42
             _SCREAMING_SNAKE_CASE							      = -100





             def __init__(			self ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		lowercase = None ,		lowercase=False ,		lowercase = Split.train ,		)						->	Any:
                   lowerCAmelCase    		=			token_classification_task.read_examples_from_file(lowercase ,		lowercase					)
                   # TODO clean up all this to leverage built-in features of tokenizers
                   lowerCAmelCase    		=			token_classification_task.convert_examples_to_features(
                       lowercase ,		lowercase ,		lowercase ,		lowercase ,		cls_token_at_end=bool(model_type in ["""xlnet"""]					) ,		cls_token=tokenizer.cls_token ,		cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0 ,		sep_token=tokenizer.sep_token ,		sep_token_extra=lowercase ,		pad_on_left=bool(tokenizer.padding_side == """left"""					) ,		pad_token=tokenizer.pad_token_id ,		pad_token_segment_id=tokenizer.pad_token_type_id ,		pad_token_label_id=self.pad_token_label_id ,		)

                   def gen():
                         for ex in self.features:
                               if ex.token_type_ids is None:
                                     yield (
                                         {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                                         ex.label_ids,
                                     )
                               else:
                                     yield (
                                         {
                                             "input_ids": ex.input_ids,
                                             "attention_mask": ex.attention_mask,
                                             "token_type_ids": ex.token_type_ids,
                                         },
                                         ex.label_ids,
                                     )

                   if "token_type_ids" not in tokenizer.model_input_names:
                         lowerCAmelCase    		=			tf.data.Dataset.from_generator(
                             lowercase ,		({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa) ,		(
                                 {"""input_ids""": tf.TensorShape([None]					), """attention_mask""": tf.TensorShape([None]					)},
                                 tf.TensorShape([None]					),
                             ) ,		)
                   else:
                         lowerCAmelCase    		=			tf.data.Dataset.from_generator(
                             lowercase ,		({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa, """token_type_ids""": tf.intaa}, tf.intaa) ,		(
                                 {
                                     """input_ids""": tf.TensorShape([None]					),
                                     """attention_mask""": tf.TensorShape([None]					),
                                     """token_type_ids""": tf.TensorShape([None]					),
                                 },
                                 tf.TensorShape([None]					),
                             ) ,		)





             def        _snake_case     (			self					)						->	List[str]:
                   lowerCAmelCase    		=			self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features					)					)					)

                   return self.dataset





             def __len__(			self					)						->	Optional[int]:
                   return len(self.features					)





             def __getitem__(			self ,		lowercase					)						->	InputFeatures:
                   return self.features[i]






